Multi-fidelity modelling approach for airline disruption management using simulation

Disruption to airline schedules is a key issue for the industry. There are various causes for disruption, ranging from weather events through to technical problems grounding aircraft. Delays can quickly propagate through a schedule, leading to high financial and reputational costs. Mitigating the impact of a disruption by adjusting the schedule is a high priority for the airlines. The problem involves rearranging aircraft, crew and passengers, often with large fleets and many uncertain elements. The multiple objectives, cost, delay and minimising schedule alterations, create a trade-off. In addition, the new schedule should be achievable without over-promising. This thesis considers the rescheduling of aircraft, the Aircraft Recovery Problem. The Aircraft Recovery Problem is well studied, though the literature mostly focusses on deterministic approaches, capable of modelling the complexity of the industry but with limited ability to capture the inherent uncertainty. Simulation offers a natural modelling framework, handling both the complexity and variability. However, the combinatorial aircraft allocation constraints are difficult for many simulation optimisation approaches, suggesting that a more tailored approach is required. This thesis proposes a two-stage multi-fidelity modelling approach, combining a low-fidelity Integer Program and a simulation. The deterministic Integer Program allocates aircraft to flights and gives an initial estimate of the delay of each flight. By solving in a multi-objective manner, it can quickly produce a set of promising solutions representing different trade-offs between disruption costs, total delay and the number of schedule alterations. The simulation is used to evaluate the candidate solutions and look for further local improvement. The aircraft allocation is fixed whilst a local search is performed over the flight delays, a continuous valued problem, aiming reduce costs. This is done by developing an adapted version of STRONG, a stochastic trust-region approach. The extension incorporates experimental design principles and projected gradient steps into STRONG to enable it to handle bound constraints. This method is demonstrated and evaluated with computational experiments on a set of disruptions with different fleet sizes and different numbers of disrupted aircraft. The results suggest that this multi-fidelity combination can produce good solutions to the Aircraft Recovery Problem. A more theoretical treatment of the extended trust-region simulation optimisation is also presented. The conditions under which a guarantee of the algorithm's asymptotic performance may be possible and a framework for proving these guarantees is presented. Some of the work towards this is discussed and we highlight where further work is required. This multi-fidelity approach could be used to implement a simulation-based decision support system for real-time disruption handling. The use of simulation for operational decisions raises the issue of how to evaluate a simulation-based tool and its predictions. It is argued that this is not a straightforward question of the real-world result being good or bad, as natural system variability can mask the results. This problem is formalised and a method is proposed for detecting systematic errors that could lead to poor decision making. The method is based on the Probability Integral Transformation using the simulation Empirical Cumulative Distribution Function and goodness of fit hypothesis tests for uniformity. This method is tested by applying it to the airline disruption problem previously discussed. Another simulation acts as a proxy real world, which deviates from the simulation in the runway service times. The results suggest that the method has high power when the deviations have a high impact on the performance measure of interest (more than 20%), but low power when the impact is less than 5%

Airline Disruption Recovery Using Symbiotic Simulation and Multi-fidelity Modelling

The airlines industry is prone to disruption due to various causes. Whilst an airline may not be able to control the causes of disruption, it can reduce the impact of a disruptive event, such as a mechanical failure, with its response by revising the schedule. Potential actions include swapping aircraft, delaying flights and cancellations. This paper will present our research into how symbiotic simulation could potentially be used to improve the response to a disruptive event by evaluating potential revised schedules. Due to the large solution space, exhaustive searches are infeasible. Our research is investigating the use of multi-fidelity models to help guide the search of the optimisation algorithm, leading to good solutions being generated within the time constraints of disruption management

Virtual Integration Platforms (VIP) –A Concept for Integrated and Interdisciplinary Air Transportation Research and Assessment

The paper descibes a new methodology for a holistic development of air transportation concepts. The Virtual Integration Plattform (VIP) concept is based on an IT tool chain as well as human collaborative methods to deal with complex systems. As a result the definitions of future air transportation concepts for short range "Quiet and Clean", long range "Comfortable and Clean" and individual transport "Fast and Flexible" are presente

Combining symbiotic simulation systems with enterprise data storage systems for real-time decision-making

This is the author accepted manuscript. The final version is available from Taylor & Francis via the DOI in this recordA symbiotic simulation system (S3) enables interactions between a physical system and its computational model representation. To support operational decisions, an S3 uses real-time data from the physical system, which is gathered via sensors and saved in an enterprise data storage system (EDSS). Both real-time and historical data are then used as inputs to the different components of an S3. This paper proposes a generic system architecture for an S3 and discusses its integration within EDSSs. The paper also reviews the literature on S3 and analyses how these systems can be used for real-time decision-making.Erasmus

Operational Research: Methods and Applications

Throughout its history, Operational Research has evolved to include a variety of methods, models and algorithms that have been applied to a diverse and wide range of contexts. This encyclopedic article consists of two main sections: methods and applications. The first aims to summarise the up-to-date knowledge and provide an overview of the state-of-the-art methods and key developments in the various subdomains of the field. The second offers a wide-ranging list of areas where Operational Research has been applied. The article is meant to be read in a nonlinear fashion. It should be used as a point of reference or first-port-of-call for a diverse pool of readers: academics, researchers, students, and practitioners. The entries within the methods and applications sections are presented in alphabetical order. The authors dedicate this paper to the 2023 Turkey/Syria earthquake victims. We sincerely hope that advances in OR will play a role towards minimising the pain and suffering caused by this and future catastrophes

Delay propagation – new metrics, new insights

Network delay propagation is intimately linked with the challenges of managing passenger itineraries and corresponding connections. Airline decision-making governing these processes is driven by operational and regulatory factors. Using the first European network simulation model with explicit passenger itineraries and full delay cost estimations, we explore these factors through various flight and passenger prioritisation rules, assessing the performance impacts. Delay propagation is further characterised under the different prioritisation rules using complexity science techniques such as percolation theory and network attack. The relative effects of randomised and targeted disruption are compared

Collaborative airport passenger management with a virtual control room

Key performance indicator-driven connection management at airports with public transportation services Integrated traffic management across a range of shareholders within a widespread network requires a definition of KPIs to assess intermodal performance. Their purpose is to monitor and analyze the technical performance of individual modules of a transportation network, e.g. an airport. Actions recommended to optimize operations and to maintain operation during disruptions are ideally based on an understanding of the system-wide impact of the action and for the entire intermodal chain of the journey from door to door. With all the numerous possible parameters and indicators which can be monitored within a complex transportation network, not every indicator is necessarily a key indicator. We show which indicators can depict a situation consisting of a system status and a system forecast, which allow interstakeholder optimization and which serve as an enabler for a Mobility as a Service (MaaS) concept. Examples of intermodal-oriented KPIs include the Amount of useable travel time, the Boarding Score and the Connectivity Matrix. Useable travel times are defined as the longest, continuous travel and waiting times which can be used for productivity or relaxation. The Boarding Score accounts for reaching a connection on time, e.g. catching the desired flight after travelling to the airport by train. The Connectivity Matrix dynamically expands the Minimum Connecting Time MCT (which is known from airports and is important for booking systems), allowing forecast values to be offered based on the demanded connecting journeys instead of on average spreadsheet values. With the deployment of the new key performance indicator set a tool is given to visualize situational awareness at an airport. This includes nowcasting as well as forecasting awareness which is required to assess different options of intervention. The method of calculation of the KPI set is enriched by a concept of visualization using virtual reality options to maintain usability within distributed management teams. For validation purpose, the Optimode.net simulation environment is used

Operational Research: Methods and Applications

Throughout its history, Operational Research has evolved to include a variety of methods, models and algorithms that have been applied to a diverse and wide range of contexts. This encyclopedic article consists of two main sections: methods and applications. The first aims to summarise the up-to-date knowledge and provide an overview of the state-of-the-art methods and key developments in the various subdomains of the field. The second offers a wide-ranging list of areas where Operational Research has been applied. The article is meant to be read in a nonlinear fashion. It should be used as a point of reference or first-port-of-call for a diverse pool of readers: academics, researchers, students, and practitioners. The entries within the methods and applications sections are presented in alphabetical order

A Model for aircraft recovery problem

Thesis submitted in partial fulfillment of the requirements for the Degree of Master of Science in Mobile Telecommunication and Innovation at (MSc.MTI) at Strathmore UniversityIn the airline industry, a myriad of uncertain events take place that lead to the disruption of original flight schedules. Such events include mechanical failure, technical challenges, weather changes, airport and crew related issues. Airlines therefore need a robust, dynamic way of recovering their schedules during disruptions in order to remain profitable. In recovery scenarios, aircraft recovery is given the highest priority since aircraft are the scarcest and most utilised resources in the airline. A mathematical model for airline schedule recovery that recovers aircrafts was presented in this study. The model is based on defining a recovery scope once a fleet of aircraft has been disrupted. The model examines the possibility of delaying the flights for a short period, reassigning aircraft, ferrying aircraft and also cancelling flights. The objective of the model is to minimise costs associated with assigning a different aircraft to the disrupted flight leg, delay costs, cancellation costs for business class passengers, cancellation costs for economy class passengers and ground costs. This study uses real time data from Kenya Airways to test the proposed model. A decision support system was then developed and deployed to the Integrated Operations Control Centre in Kenya Airways for use by the duty managers to come up with optimal solutions with the least cost implications to the airline